Intermediate SQL for Data Analytics Course

Course Overview

• Designed for those with basic SQL knowledge aiming to advance their skills.
• Short 10-minute lessons with real-world exercises.
• Includes interview-level practice problems to prepare for jobs.
• By the end, you'll build a customizable portfolio project.

Importance of SQL

• SQL is the most popular tool among data analysts in the U.S.
• Essential for roles such as data analysts, data engineers, and data scientists.
• SQL is used to communicate with databases.
• Widely used with Python, Excel, PowerBI, and Tableau.

Course Structure

• First Half:
  • Intro and setup with the database.
  • Pivoting data using case statements.
  • Transforming and analyzing data using aggregation and statistical methods.
  • Intermediate date and time functions.
  • Window functions covering basic and complex aggregations.
• Second Half:
  • Setting up PostgreSQL locally.
  • Building a portfolio project within PostgreSQL.
  • Learning data transformation functions.
  • Query optimization and using keywords like 'EXPLAIN'.

Course Resources

• Free course with linked resources.
• Additional resources for contributors.
• Community access for Q&A and community engagement.
• Certificate of completion available for supporters.

Common Questions

• Database Used: PostgreSQL, noted for being the most admired and desired database.
• Running SQL Commands:
  • Using Google Colab for the first half, setting up a Jupyter notebook environment.
  • PG Admin and DBeaver for local database setup and management.

Tools and Environment

• Google Colab
  • Setup to run SQL commands in a Jupyter notebook.
  • Use of code cells for SQL query execution.
  • Integration with Python for data visualization.
• PostgreSQL
  • Local installation on your machine for the second half.
  • Running queries using PG Admin and DBeaver for more advanced database management.

Key Learning Points

• SQL Skills for Data Analytics:
  • Understanding and using advanced SQL concepts.
  • Developing skills for data transformation and analysis.
  • Building and optimizing complex SQL queries.
  • Using SQL for real-world data projects.

Conclusion

• The course emphasizes open-source education.
• Provides tools and resources to advance SQL skills.
• Prepares students for higher-level data analytics roles through practical application and project-based learning.